Predictive model for progressive salinization in a coastal aquifer using artificial intelligence and hydrogeochemical techniques: a case study of the Nile Delta aquifer, Egypt

Ahmed M Nosair; Mahmoud Y Shams; Lobna M AbouElmagd; Aboul Ella Hassanein; Alan E Fryar; Hend S Abu Salem

doi:10.1007/s11356-021-16289-w

Predictive model for progressive salinization in a coastal aquifer using artificial intelligence and hydrogeochemical techniques: a case study of the Nile Delta aquifer, Egypt

Environ Sci Pollut Res Int. 2022 Feb;29(6):9318-9340. doi: 10.1007/s11356-021-16289-w. Epub 2021 Sep 9.

Authors

Ahmed M Nosair¹, Mahmoud Y Shams², Lobna M AbouElmagd³, Aboul Ella Hassanein⁴, Alan E Fryar⁵, Hend S Abu Salem⁶

Affiliations

¹ Environmental Geophysics Lab (ZEGL), Geology Department, Faculty of Science, Zagazig University, Zagazig, Egypt.
² Faculty of Artificial Intelligence, Kafrelsheikh University, KafrelSheikh, 33511, Egypt.
³ Misr Higher Institute for Commerce and Computers, MET, Mansoura, Egypt.
⁴ Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt.
⁵ Department of Earth and Environmental Sciences, University of Kentucky, Lexington, USA. alan.fryar@uky.edu.
⁶ Geology Department, Faculty of Science, Cairo University, Cairo, Egypt.

PMID: 34499306
DOI: 10.1007/s11356-021-16289-w

Abstract

To monitor groundwater salinization due to seawater intrusion (SWI) in the aquifer of the eastern Nile Delta, Egypt, we developed a predictive regression model based on an innovative approach using SWI indicators and artificial intelligence (AI) methodologies. Hydrogeological and hydrogeochemical data of the groundwater wells in three periods (1996, 2007, and 2018) were used as input data for the AI methods. All the studied indicators were enrolled in feature extraction process where the most significant inputs were determined, including the studied year, the distance from the shoreline, the aquifer type, and the hydraulic head. These inputs were used to build four basic AI models to get the optimal prediction results of the used indicators (the base exchange index (BEX), the groundwater quality index for seawater intrusion (GQI_SWI), and water quality). The machine learning models utilized in this study are logistic regression, Gaussian process regression, feedforward backpropagation neural networks (FFBPN), and deep learning-based long-short-term memory. The FFBPN model achieved higher evaluation results than other models in terms of root mean square error (RMSE) and R² values in the testing phase, with R² values of 0.9667, 0.9316, and 0.9259 for BEX, GQI_SWI, and water quality, respectively. Accordingly, the FFBPN was used to build a predictive model for electrical conductivity for the years 2020 and 2030. Reasonable results were attained despite the imbalanced nature of the dataset for different times and sample sizes. The results show that the 1000 μS/cm boundary is expected to move inland ~9.5 km (eastern part) to ~10 km (western part) to ~12.4 km (central part) between 2018 and 2030. This encroachment would be hazardous to water resources and agriculture unless action plans are taken.

Keywords: Artificial intelligence; Feedforward backpropagation neural network; Groundwater contamination; Hydrogeochemical analysis; Regression models; Seawater intrusion.

MeSH terms

Artificial Intelligence*
Egypt
Environmental Monitoring
Groundwater*
Seawater